{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import json\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "layers = {\n",
    "    'mixed3a': 256,\n",
    "    'mixed3b': 480,\n",
    "    'mixed4a': 508,\n",
    "    'mixed4b': 512,\n",
    "    'mixed4c': 512,\n",
    "    'mixed4d': 528,\n",
    "    'mixed4e': 832,\n",
    "    'mixed5a': 832,\n",
    "    'mixed5b': 1024\n",
    "}\n",
    "\n",
    "with open('data/imagenet.json') as f:\n",
    "    imagenet = json.load(f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compute top channel usage statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mixed3a\n",
      "mixed3b\n",
      "mixed4a\n",
      "mixed4b\n",
      "mixed4c\n",
      "mixed4d\n",
      "mixed4e\n",
      "mixed5a\n",
      "mixed5b\n"
     ]
    }
   ],
   "source": [
    "top_channels = {}\n",
    "\n",
    "for layer in list(layers.keys()):\n",
    "    print(layer)\n",
    "    top_channels[layer] = {\n",
    "        'channels': layers[layer],\n",
    "        'channels_used': 0,\n",
    "        'channels_not_used': 0,\n",
    "        'percent_channels_used': 0\n",
    "    }\n",
    "    all_channels = []\n",
    "    \n",
    "    for c in imagenet:\n",
    "        for ch in c['topChannels'][layer]:\n",
    "            all_channels.append(ch['channel'])\n",
    "            \n",
    "    (channels, counts) = np.unique(all_channels, return_counts=True)\n",
    "    sorted_counts, sorted_channels = (list(t) for t in zip(*sorted(zip(counts, channels), reverse=True)))\n",
    "    \n",
    "    top_channels[layer]['channels_used'] = int(len(np.unique(sorted_channels)))\n",
    "    top_channels[layer]['channels_not_used'] = layers[layer] - top_channels[layer]['channels_used']\n",
    "    top_channels[layer]['percent_channels_used'] = top_channels[layer]['channels_used'] / layers[layer]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mixed3a\n",
      "{'channels': 256, 'channels_used': 140, 'channels_not_used': 116, 'percent_channels_used': 0.546875}\n",
      "mixed3b\n",
      "{'channels': 480, 'channels_used': 243, 'channels_not_used': 237, 'percent_channels_used': 0.50625}\n",
      "mixed4a\n",
      "{'channels': 508, 'channels_used': 393, 'channels_not_used': 115, 'percent_channels_used': 0.7736220472440944}\n",
      "mixed4b\n",
      "{'channels': 512, 'channels_used': 507, 'channels_not_used': 5, 'percent_channels_used': 0.990234375}\n",
      "mixed4c\n",
      "{'channels': 512, 'channels_used': 510, 'channels_not_used': 2, 'percent_channels_used': 0.99609375}\n",
      "mixed4d\n",
      "{'channels': 528, 'channels_used': 526, 'channels_not_used': 2, 'percent_channels_used': 0.9962121212121212}\n",
      "mixed4e\n",
      "{'channels': 832, 'channels_used': 713, 'channels_not_used': 119, 'percent_channels_used': 0.8569711538461539}\n",
      "mixed5a\n",
      "{'channels': 832, 'channels_used': 832, 'channels_not_used': 0, 'percent_channels_used': 1.0}\n",
      "mixed5b\n",
      "{'channels': 1024, 'channels_used': 1024, 'channels_not_used': 0, 'percent_channels_used': 1.0}\n"
     ]
    }
   ],
   "source": [
    "for layer in top_channels:\n",
    "    print(layer)\n",
    "    print(top_channels[layer])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inspect top channels of the top channels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mixed3a\n"
     ]
    },
    {
     "data": {
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rzItdmWa2S7ftKZl83nKZz7vOmsfuXOK4uRbM3YP74rx4mSQVn9XVYUcm559l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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113d4f828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mixed3b\n"
     ]
    },
    {
     "data": {
      "image/png": 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A2IkUrwA73FYUwwpsAGDdFK8AjJ5/YQQA+M0rAAAAo3fEz7xW1blJfjLJMUne0lq74ki3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\nAKOneAUAAGD0FK8AAACMnuIVAACA0fv/XgmeLscbJdcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13b452860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mixed4a\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x13b448198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mixed4b\n"
     ]
    },
    {
     "data": {
      "image/png": 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BCX+e2Q80+s9JfnbN+JOnht+Ugw9I6FPmzhx8ou7TM0nuviUHb8R/SveZ37XB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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x13aed40f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mixed4c\n"
     ]
    },
    {
     "data": {
      "image/png": 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OC9p6U8fyGejbGXFMu6yMOfr46cQkH8iRN9Y7f1HbHyt/z3oBjrW/DZ3wq9J9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\nUAEAAJgECSoAAACT8P8Bn3HdDIC0/nEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11439b518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mixed4d\n"
     ]
    },
    {
     "data": {
      "image/png": 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OSZKkR4z33UrSwTGJ/4O6DnhZRNwB/HL5XpIkSZKkvlr/H1SAzLyO4mm9ZObfAKeNo1xJ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\nWINt9L9QPDSpdZ8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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11452ca90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mixed4e\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d928780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mixed5a\n"
     ]
    },
    {
     "data": {
      "image/png": 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+/T+R5K4kb+rn/Y5NMZ8zJd7xST6a5GGbpv9muu8LDG3Pi7O1u/i+Lt3lLp/q\ny/qiLSx7RBlnrMdF6jxtHf7XdN+RuCFdh3XKFrfLT6XrBG/sYz0os9v3y9J9Wvve9HfoW7aMC8b7\n8nQ/U3BDX8aNu+vOKuML87mfInj5IvFmtZ1l4qX7yZC/6v8uSVJb6SMWiP+7/fMb0v3MwKlL9G1n\nZ+IuvkP1XyLuN/f1fn/6/miF/WVW2zk7A/3iUFucs53nxRvaFlP72iQPTXfm4KZ0g5cfmVP//9nP\nd32Sc5bdzgPrcWp7zAL9xEC8oX5sX7p9+eZ0++UXLRhv6v6cbvD98dz7ZzEesSneEX30UH37+Wf2\nOUNtZ0ad527ngXhDx7+5de7nm9Z3r3JMnXmsy8S4ZJHtPK2fyUB7zhqOVwvWeVpbvCXdd/s21v1v\nTLy2bNtZqo/I8PhuVvs+OzP6sWX62ol5Ls70u/guXOdF1uO0eDPa4tztPKOMQ+1x2fHY1P1vwToP\nHV+m9juLtsej8W/j4AQAAAD3K5f4AgAAMAoSVAAAAEZBggoAAMAoSFABAAAYBQkqAAAAoyBBBQAA\nYBQkqAAAAIyCBBUAAIBR+P+njwnjgwFLsgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d74cbe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mixed5b\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1362efcc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "k = 4\n",
    "end_idx = 40\n",
    "\n",
    "top_channels = {}\n",
    "\n",
    "for layer in list(layers.keys()):\n",
    "    print(layer)\n",
    "    top_channels[layer] = {\n",
    "        'channels': layers[layer],\n",
    "        'channels_used': 0,\n",
    "        'channels_not_used': 0,\n",
    "        'percent_channels_used': 0\n",
    "    }\n",
    "    all_channels = []\n",
    "    \n",
    "    for c in imagenet:\n",
    "        for ch in c['topChannels'][layer][0:k]:\n",
    "            all_channels.append(ch['channel'])\n",
    "            \n",
    "    (channels, counts) = np.unique(all_channels, return_counts=True)\n",
    "    sorted_counts, sorted_channels = (list(t) for t in zip(*sorted(zip(counts, channels), reverse=True)))\n",
    "\n",
    "    plt.figure(figsize=(16,4))\n",
    "    plt.bar(\n",
    "        range(len(sorted_channels[0:end_idx])),\n",
    "        np.array(sorted_counts[0:end_idx]),\n",
    "        align='center'\n",
    "    )\n",
    "    plt.xticks(range(len(sorted_channels[0:end_idx])),[str(x) for x in sorted_channels[0:end_idx]])\n",
    "    plt.show()\n",
    "    \n",
    "    top_channels[layer]['channels_used'] = int(len(np.unique(sorted_channels)))\n",
    "    top_channels[layer]['channels_not_used'] = layers[layer] - top_channels[layer]['channels_used']\n",
    "    top_channels[layer]['percent_channels_used'] = top_channels[layer]['channels_used'] / layers[layer]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
